What is Big Data and Why is It Important?
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What is Big Data and Why is It Important?

Big data is larger, more complex data sets, especially from new data sources. These data sets are so voluminous that traditional data processing software just can’t manage them. But these massive volumes of data can be used to address business problems you wouldn’t have been able to tackle before.
Published: May 03, 2022
What is Big Data and Why is It Important?

What is the Definition of Big Data?

Big data is a combination of unstructured, semi-structured, and structured a data collected by organizations that can be mined for information and used in machine learning projects, predictive modeling, and other advanced analytics applications.

Big data is often characterized by the three V's:
the large volume of data in many environments;
the wide variety of data types frequently stored in big data systems; and
the velocity at which much of the data is generated, collected and processed.

More recently, several other V's have been added to different descriptions of big data, including veracity, value and variability. Although big data does not equate to any specific volume of data, big data deployments often involve terabytes, petabytes, and even exabytes of data created and collected over time.

Why Big Data Matters?

Companies use big data in their systems to improve operations, provide better customer service, create personalized marketing campaigns, and take other actions that, ultimately, can increase revenue and profits. Businesses that use it effectively hold a potential competitive advantage over those that don't because they're able to make faster and more informed business decisions.

Here are some more examples of how big data is used by organizations:
In the energy industry, big data helps oil and gas companies identify potential drilling locations and monitor pipeline operations; likewise, utilities use it to track electrical grids.
Financial services firms use big data systems for risk management and real-time analysis of market data.
Manufacturers and transportation companies rely on big data to manage their supply chains and optimize delivery routes.
Other government uses include emergency response, crime prevention and smart city initiatives.

Types of Big Data

Following are the types of Big Data:

Unstructured
Any data with unknown form or the structure is unstructured data. In addition to the size being huge, unstructured data poses multiple challenges in terms of its processing for deriving value out of it. A typical example of unstructured data is a heterogeneous data source containing a combination of simple text files, images, videos etc. Now day organizations have wealth of data available with them but unfortunately, they don’t know how to derive value out of it since this data is in its raw form or unstructured format.

Semi-structured
Semi-structured data can contain both the forms of data. We can see semi-structured data as a structured in form but it is actually not defined. Example of semi-structured data is a data represented in an XML file.

Structured
Any data that can be stored, accessed, and processed in the form of fixed format is termed as a ‘structured’ data. Over the period of time, talent in computer science has achieved greater success in developing techniques for working with such kind of data (where the format is well known in advance) and also deriving value out of it. However, nowadays, we are foreseeing issues when a size of such data grows to a huge extent, typical sizes are being in the rage of multiple zettabytes.

How Big Data Works?

Big data gives you new insights that open up new opportunities and business models. Getting started involves three key actions:

1. Integrate
Big data brings together data from many disparate sources and applications. Traditional data integration mechanisms, such as extract, transform, and load (ETL) generally aren’t up to the task. It requires new strategies and technologies to analyze big data sets at terabyte, or even petabyte, scale. During integration, you need to bring in the data, process it, and make sure it’s formatted and available in a form that your business analysts can get started with.

2. Manage
Big data requires storage and your storage solution can be in the cloud, on premises, or both. You can store your data in any form you want and bring your desired processing requirements and necessary process engines to those data sets on an on-demand basis. Many people choose their storage solution according to where their data is currently residing. The cloud is gradually gaining popularity because it supports your current compute requirements and enables you to spin up resources as needed.

3. Analyze
Your investment in big data pays off when you analyze and act on your data. Get new clarity with a visual analysis of your varied data sets. Explore the data further to make new discoveries. Share your findings with others. Build data models with machine learning and artificial intelligence. Put your data to work.

Summary

Big Data: Big Data means a data that is huge in size. It is a term used to describe a collection of data that is huge in size and yet growing exponentially with time.
Big Data could be: Unstructured, semi-structured, structured. 
Big Data characteristics: Volume, variety, velocity. 

Published by May 03, 2022 Source :TechTarget, Source :Guru99, Source :Oracle

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